Action Planning based on Open Knowledge
Graphs and LOD
Seiji Koide1 , Fumihiro Kato1 , Hideaki Takeda12 , Yuta Ochiai3 , andKenki Ueda3
1

National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430,
Japan,
takeda@nii.ac.jp,
WWW home page: http://www-kasm.nii.ac.jp/~takeda/index.html
2
SOKENDAI (The Graduate University for Advanced Studies)3
Toyota Motor Corporation

Abstract. In this preliminary report, we show how action planning isrealized by using LOD datasets, e.g., Linked Geo Data, DBpedia, WordNet, etc. To make a recommendation for car drivers and passengers, we
combine these existing open datasets with newly constructed ontologies
of facilities and services. We develop the inference procedure to translateuser requests into SPARQL queries to obtain a recommendation on appropriate facilities for users. Common sense knowledge is also requiredin the reasoning process.
Keywords: DBpedia, LinkedGeoData, Knowledge-based system

1

Introduction

While Linked Data is now gradually growing to be the infrastructure of coming
Knowledge Society, we are still struggling to show the potential of Linked Data
to most people in the society including basic industries. To cope with this situation and propel the deployment of Semantic Web technology, it is needed todemonstrate the performance of linking distinct datasets and show the usefulnessof outbound and inbound linking data beyond enterprise data in diverse applications. Yet there is no linking data among large linked datasets such as DBpedia,
Freebase, and OpenCyc from the viewpoint of LOD applications, although eachcollection of them are a kind of isolated showcase of LOD with internally linked
data within their own territories and objectives.
In our use-case, the system accepts ambiguous requests from car drivers andpassengers, plans driver actions to achieve goals that satisfies the requests, including alternatives, and makes a recommendation for the drivers and passengers.
In this preliminary work, we have found that it is required more goal-oriented
linked datasets and common sense knowledge as bridge between isolated LOD
datasets existing. We have also found that Semantic Web technology or specifically RDF stores and SPARQL engines are enough as enabling technology tocreate and demonstrate new applications based on heterogeneous and diverse
datasets.
To obtain driving destinations as goal, we arranged Linked Geo Data andDBpedia Japanese[1] with newly constructed facility ontology and service ontology, which make links among such existing datasets. Japanese WordNet[2] is alsoutilized as general knowledge, because it was necessary to make the inferencewith common sense to discover destinations from user requests. We developed
the inference procedure to translate user requests into SPARQL queries to obtaina recommendation on appropriate facilities for users.
The purpose of this preliminary report is to make a clear direction for development of LOD applications in order to deploy linked data as the infrastructureof society in future. The structure of this preliminary paper is as follows. We
describe the detail of the use-case in Section 2. Section 3 reports the related workfrom the viewpoint of the long-term research activity on artificial intelligence.
In Section 4, we show how we realize new ontologies on facilities and services
in order to utilize open knowledge-bases and LOD. Section 5 describes the systematization of multiple linked datasets and the SPARQL endpoint. Section 6
describes the inference procedure for the purpose of getting recommendations
by using SPARQL queries. We show how we can realize action planning by using
open knowledge-bases and LOD. Section 7 reports a simple example of execution
by this prototype system that realizes a new purpose-oriented inference engine
with SPARQL and large-scale open knowledge-bases. Section 8 provides discussions. In the last section, we summarize the results and address the future work
toward the new era of Knowledge Society based on Open Data and Linked Data.

2

Problem Setting for the Use CaseIn setting of the use-case, we firstly made more than ten scenarios of conversation
between users and this system. In each case, a user in a car speaks a single ora number of requests to do something with driving a car. Then, the systemanalyzes the requests under the consideration of current contexts such as time,location, driving time, etc. At last, the system makes concrete action proposals
to visit specific points (shop, facility, etc.) or areas (sightseeing area, good place
for time-consuming, etc.) with a reasonable visiting order. Basically, the request
may be vague and complex, but the recommendation is specific and concrete.
However, every recommendation is a sequence of actions, and proposed actions
are quite limited within these scenarios, for example, drive somewhere, buy or
eat something, do some sport, and so on. One of the simplest scenarios is asfollows.
Child passenger(hereafter C): I want to see a lion.
System(hereafter S): How about Ueno Zoo. A baby lion was born recently.
C: It sounds good, but I was there last month.
S: Well, how about Kinoshita Circus. You can see a lion show there.
C: OK. That’s fine.

In this scenario, the system must discover the knowledge that a lion is akind of animal and a zoo is a public entertainment facility for seeing animals.
The system must find out a nearest zoo, that is Ueno Zoo in this case, fromthe current location, and must reason that users have enough time to drive tothe destination and walking around the zoo. Furthermore, due to the negative
response of the user, the system must discover a neighboring circus that presents
a lion show as an alternative.

3

Related Work

Planning was one of the most popular AI research area in the 1970s through the
1980s, where the research eﬀorts focused on reasoning mechanisms on making
plans [3][4][5], but the role of knowledge in inference was not regarded. Aroundthe 1980s, the reasoning with knowledge was well studied in problem solving,
and the eﬀorts how to use human experts’ knowledge with inference engines
amounted to expert systems. Even after that, we have no remarkable innovation
to solve information-rich planning problems, as expert systems was confronted
with. Note that scheduling problems in project management, production management, and delivery management are rare successes by domain specific knowledge and algorithms.
The recent success of IBM Watson in TV show Jeopardy! seems to promiseknowledge graph approach for problem solving and decision making[6]. However, we should note that Watson system for Jeopardy! did not use common
sense for combining multiple knowledge graphs. Basically, the system model for
Jeopardy! game is categorized into a Q&A system for trivial knowledge. Multiple knowledge graphs and selection of the most probable answer candidates are
key technique in Watson for Jeopardy!, and common sense knowledge is used
only in WATSONPATHS for breaking down the top level question into subquestions based on unstructured text corpus, and not used as bridge for multiple
knowledge graphs [7].
To endow computers with common sense is one of the major long-term goalsof artificial intelligence. Common sense reasoning widely ranges over a numberof diﬀerent fields from taxonomic reasoning, geographic reasoning, temporal reasoning, reasoning about actions and changes, qualitative reasoning [8] to naive
physics, interpersonal interaction theory, and social relationship theory. Mueller[7] described common sense reasoning based on event calculus. The knowledgebased approach of common sense reasoning is categorized by Davis [8] into five
types as i) Math-based, ii) Informal, iii) Large-scale, iv) Web mining, and v)
Crowd Sourcing, then he discussed pros and cons of each approach. In this research, our approach is classified as Informal and Large-scale, while DBpedia
can be classified into the approach of Crowd Sourcing.
The role of verb is not seriously regarded in action planning so far. Action
is just called operator in the context of old AI planning. Cognitive Linguistics
pays more careful attention on the relation between verbs and objectives. In
this research work, we picked up several verbs such as ‘see’, ‘eat’, and ‘buy’ inorder to plan actions according to the use-case scenarios, and the relation ofsuch verbs to objectives is realized in our facility ontology and service ontology.
See the details in the following section.
Schank addressed eleven primitive actions in Conceptual Dependency theory [9]. He suggested us verbs may be categorized in hierarchy structure. Schankalso invented the idea of script that explains typical stereotyped human behaviors at restaurants or fast food shops or other facilities [10]. We also defined
typical behavior for users in our facility ontology and service ontology, where anoodle shop as food facility provides noodle food service, and the noodle foodservice is composed of eat action and food noodle as objective.
Frame theory by Fillmore is a theory for Natural Language Understanding [11]. In Filmore’s semantic frame, verb ‘buy’ is described by other framessuch as ‘goods’ as object, ‘buyer’ as subject, in addition to other frames ‘seller’and ‘money’. Extending semantic frame theory, Fillmore developed Case Grammar, in which a sentence is analyzed with two type cases, surface cases anddeep cases. Fillmore addressed several deep cases, Agent, Object, Instrumental,
Result, Locative, etc. In this work, we also adopted case grammar for text processing, because Japanese is very compatible to Case Grammar, and it is easy
to apply surface cases to Japanese particles. See the details in Section 6.
Levin [12] published a resource materials on the English verb lexicon, in whichverbs in English are classified into a number of verb classes (by attributes), butthere is no hierarchy of classes and no ontological or taxonomic description about
verbs, and less descriptions on the relationship to objectives.
Generally, we have a number of aspects in dialogue. Searle described themechanism of human speech interaction and addressed the Speech Act theory[13]. In his theory, he follows the idea of John L. Austin and elaborated ten
speech aspects of illocutionary act, that is a terminology for intensive action by
speech, i.e., request, question, assert, state, aﬃrm, thank, advice, warn, greet, andcongratulate. Today, chat should be taken account of in addition. In this preliminary work, we took account of only request. See the details of text processing in
Section 6.

4

Ontologies for Facility, Action Target, and Service

4.1

How to Make Facility Ontology

Instead of directly searching individual facilities like Ueno Zoo or individual
shops like Yodobashi Akiba store (a home electric appliance mass retailer inJapan), we considered classes of facilities like zoo or home electric appliancemass retailer to make the system scalable, then made a facility ontology that
contains typical facilities and we defined typical users’ behavior at such facilities
like “a user sees animals in a zoo” or “a user buys a household appliance at
a home electric appliance mass retailer.” Even if we accidentally fail to guidean actual facility that satisfies user’s special requests, such a problem will besolved with the development of richer and more specific datasets that includes
individual facilities.

The facility ontology is constructed mainly by extracting facility classes related to leisure and meals in Linked Geo Data (LGD). LGD constructs a shallow
class hierarchy from tags attached to the nodes and ways of OpenStreetMap(OSM). Therefore, LGD classes makes it easy to incorporate new facilities andnew facility types.
On the other hand, as a result of adopting LGD / OSM, duplicates of classes
due to notation fluctuation of tags and the low coverage rate of actual facilities
at the instance level could be a big problem. However, we think this approachis the best for our purpose in our best knowledge, because the LGD / OSM is
the largest facility data that is freely available at the present. Also note that
actually it is impossible to measure how much the existing facilities are covered
in reality. Regarding duplicates of classes in LGD, we select an entity as primaryclass that has both the most information-rich descriptions on the OSM and alarge number of instances, then the rest are associated with owl:equivalentClassto the primary class.
The following shows an example of zoo class in the facility ontology. The
meanings of Japanese words are added here in English as turtle comments forreaders. Both a service of “see animal” and “pay admission fee for cultural facility” are actually described in the service ontology as subclasses of “see” service
and “admission-viewing-gaming” service. Note that each service is described as apair of an action and an action target, which users can perform. In this paper, we
manually acquired and created service knowledge of facilities within the scenarios
as necessary. See the statistic numbers in Table 1. As shown below, the lgdo:Zoo
class is linked to the dbo:Zoo class in DBpedia Ontology to make possible tosearch related facility instances in DBpedia Japanese. The dbo:Zoo already hasa link to Wikidata’s wikidata:Q43501. Thus, it can be easily expanded when
Wikidata is added.
lgdo:Zoo a owl:Class;servicevoc:dbpediaClass dbo:Zoo ;servicevoc:provideService [ servicevoc:hasService [servicevoc:action action:払う;# pay
servicevoc:target target:文化施設入場料 ], [ # admission fee# for cultural facility
servicevoc:action action:見る;# seeservicevoc:target target:動物 ]] ;# animalrdfs:subClassOf servicevoc:Facility .
4.2

How to Make Service Ontology

In the facility ontology, a number of services corresponding to distinct facilities
come up with common abstract services. For example, both museums and art
museums have the same service of “paying entrance fee for cultural facilities”. In
addition, there are hierarchical relationships among users’ action targets, thenwe have a similar relationship between services. For example, “seeing animals”
can be regarded as the top of “looking at a lion”. We constructed an ontology ofservices apart from facility classes, so that services are independently recognizable, and it enabled us to expand the performance of inference by applying thehierarchy of services. In this paper, the part of service ontology is constructedby using the Classification in the Household Survey of the Ministry of Internal
Aﬀairs and Communications. The top of service ontology is the ‘facility service’and it is related to aspects of two types of behaviors, namely, ‘purchase service’
focused on purchasing behavior, and an ‘activity service’ focused on the otherbehaviors at facilities. The following shows an example of ‘purchase service’ ontology entries.
service:食料_サービス a owl:Class;# food service
rdfs:label "食料_サービス";
servicevoc:action action:買う;# buy
servicevoc:target target:食料;# foodrdfs:subClassOf service:購買_サービス . # purchase service
service:肉類_サービス a owl:Class;# meat service
rdfs:label "肉類_サービス";
servicevoc:action action:買う;# buy
servicevoc:target target:肉類;# meatrdfs:subClassOf service:食料_サービス . # food service

4.3

How to Make Target Ontology

For the sake of systematical description of actions and action targets, we usedthe Household Income Balance Item Classification List (January, 2015) of theStatistics Bureau of the Ministry of Internal Aﬀairs and Communications, ofwhich items of statistics data are used to describe purchasing behavior at facilities. User’s behavior at facilities can be divided into purchasing behavior (suchas buying something or paying for some benefits as service) and the other actions(see, eat, drink, etc.). This classification is based on a hierarchical structure ofaction targets as users’ behavior as consumer, so it is possible to consider cooperation with statistical data in future, starting with purchase actions. For actions
and action targets other than purchasing behavior, we used Japanese WordNet,because we want to use WordNet’s knowledge on the relationship between eachverb as action and each noun as an action target. For instance, we made Action
Target Ontology as follows.
target:動物 rdfs:label "動物";# animalservicevoc:wordnet wnja11instances:word-動物 .
target:食料 a owl:Class; rdfs:label "食料";# foodservicevoc:wordnet wnja11instances:word-食料 ;rdfs:subClassOf target:購買対象 .
# purchase object

4.4

LOD, Ontologies, and Statistics

All LOD datasets and ontologies in this study and their statistics data are described in Table 1.
Table 1. Outline of Prepared Datasets and Used Datasets
DatasetVersion Num. triples Num, classes used
Fact Dataset
DBpedia core+en
2016-04-01 1,131,657,931△
DBpedia Japanese
2017-02-20 113,299,748○
LinkedGeoData
2015-11-02 1,216,560,762○
General Ontology
DBpedia Ontology
2016-11-01
30,793758 ○LGD Ontology
2014-09-09
24,5301,200 ○
Japanese WordNet
2013-06-26
4,003,288
57,238 ○Japanese Wikipedia Ontology 2013-11-07
21,863,327
166,397 YAGO
3.0.2 1,001,461,792
5,130,031 OpenCyc
2012-05-10
5,783,451
233,644 UMBEL
1.5
392,728
33,686 Service Ontology
Facility Ontology
2017-02-20
3,257
418 ○
Service Ontology
2017-02-20
3,933
750 ○
Action Target Ontology
2017-02-20
2,030
622 ○
Action Ontology
2017-02-20
153
55 ○subtotal of Service Ontologies
9,373
1,845
Total
3,495,087,723
5,624,799

The service ontology at the bottom of the table is the ontology we constructed
this time, as explained in the above.
While the number of data with longitude and latitude were respectively
26,351,904, 100,139, and 1,014,836 for LinkedGeoData, DBpedia Japanese, andDBpedia respectively, the number of geodata, of which each is close to rectangle, in domestic portion excluding Hokkaido, is respectively 538,878, 67,199, and15,409.

5

Building Knowledge Graphs

We have collected a number of open knowledge resources as shown at the upper
part of Table 1, and all of them are stored in one RDF store. However, at the timeof this writing, we have actually used only DBpedia Japanese, LinkedGeoData,
Japanese WordNet, and DBpedia Ontology as open datasets. Wikidata is notstored because of the capacity.
The system used one endpoint built with one dedicated RDF store.

6

Reasoning and Q&A Process

In this preliminary research, we process natural sentences only within the range
expected at use-cases. Furthermore, in this paper it is assumed that the input
is transcribed as text instead of speech.
6.1

Process Flow and Reasoning

Work flow of this system is described as follows (see, Figure 1).
1. Input a text of user’s requests.
2. Perform the morphological analysis for the input text.
3. Perform the case analysis starting with surface cases to deep cases.
4. Translate the requests into SPARQL queries.
5. Obtain the reply of SPARQL queries.
6. Generate the answering text from the obtained reply.
7. Output the recommendation.

Fig. 1. Data Flow in Processing

Japanese is a kind of agglutinative languages and a Japanese sentence iswritten without a space left among phrases and words. A noun phrase is composed of a noun and a particle, a verb phrase is composed of a stem of verb and agrammatical conjugation. So, morphological analysis is requisite in Japanese text
processing in order to separate a sentence into phrases and words. Furthermore,particles attached to nouns decide the grammar case. For example, in responseto an user’s input “ライオンが見たいな (I want to see a lion)”, the morphologicalanalysis and shift-reduce method changes the Japanese sentence into the form of((な (pos info) 8) ((たい (pos info) 6) (見 (pos info) 5)) ((が (pos info) 4) (ライオン (pos info) 0))), here (pos info) stands for a Part-of-Speech informationof each, then case analysis produces the result such as Subject:NIL, Verb:(見る (pos info) 5), Object:(ライオン (pos info) 0), toPlace:NIL, fromPlace:NIL,
Tool:NIL. Part-of-speech information obtained from morphological analysis iseﬀectively used in various ways. For example, if there is an auxiliary verb ‘たい(want)’ next to a form of a behavioral verb such as ‘見る (see)’ or ‘食べる (eat)’,the whole sentence is interpreted as request. Thus, a request of seeing a lion is
captured and transformed into a SPARQL query to the endpoint.
From the interpretation of request (see lion), the system searches facilities
that can see a lion, using action target ontology and facility ontology. However,
we have no common sense as LOD that a lion is in a zoo. When searching fails
here, WordNet is used to generalize the target to more abstract ones by searching
hypernym relations in WordNet until animal is found.
The SPARQL search picks up a number of facilities that are located near thecurrent location, and the closest one to the current location is chosen outside of
SPARQL search.
6.2

Inference with SPARQL

Initially, we attempted to make a plan by introducing IS-A logic function intoplanning based on classical state space reasoning and backward reasoning [14].
However, more than it, searching combined ontologies using one SPARQL query
easily enabled us to retrieve acceptable instances of appropriate facility fromthe action target ontology and the facility ontology without any problems inexecution speed. The LGD class according to the user’s request from the facility
ontology can be found, and once the LGD class is known, SPARQL allows direct
retrieval of the facility instance within the LGD. If there is a DBpedia class linked
from LGD, DBpedia Japanese is also automatically searched in SPARQL queries.
The current system consists of RDF Store search and inference for interpretationof user’s requests. This configuration is beneficial at usability and re-usability.
Based on SPARQL search and open resources, it is possible to expand andrefine ontology without touching the inference engine of the planning system
in applications. It is meaningful for practical application of reasoning by large
amount of data.

7

Example of Execution

The following shows an example of execution by this prototype system, see theadded comments translated into English for readers.

SYSTEM(4): (eliza)
system> スポーツがしたいな。そのあと、温泉に行きたい。
;; I want to enjoy some sport, after that, I want to go to hot spring.
現在地はトヨタ東富士研究所です。
;; the current location is Toyota Higashfuji Institute.
スポーツをする場所を探します。; searching a location for sports......
一番近くの場所を案内します。 ; guiding the nearest place
距離は 13.37621km です。
; the distance is 13.37621km
場所：沼津市営球場; place: Numazu City Ball Park
緯度：35.1125; longitude
経度：138.863; latitude
URL："http://linkedgeodata.org/triplify/node2877270449"
現在地は (35.1125 . 138.863) です。; the current location is (35.1125 . 138.863)
温泉に入る場所を探します。
; searching a location for hot spring......
一番近くの場所を案内します。 ; guiding the nearest place
距離は 10.426165km です。
; the distance is 10.426165km
場所：伊豆長岡温泉; place: Izu-Nagaoka Hot Spring
緯度：35.0353; longitude
経度：138.929; latitude
URL："http://ja.dbpedia.org/resource/伊豆長岡温泉"
Searching for a facility in the vicinity of the current location, the Toyota
Higashifuji Institute, the system made a recommendation to go to Numazu City
Ball Park, then go to Izu-Nagaoka Hot Spring, in response to a request to go toa hot spring after enjoying some sport.
Here the command ‘eliza’ is named for just representing a mimic of Eliza
dialog system [15], that is the first dialog system in AI history. While this prototype of action planning by using open knowledge sources and SPARQL queries
is widely applicable to various kind of applications, yet there is not enough as
intelligent agent, because it has neither short-term nor long-term memory likeoriginal Eliza. Making more intelligent agent remains in future work.

8

Discussion

In this work, we captured knowledge about our world into three layers, i,e.,
factual knowledge, general knowledge, and empirical knowledge. The factual
knowledge includes objective information on individual events and matters. On
the other hand, the general knowledge is not information on individual eventsand things, but rather description of relationships among them in addition to
the abstract descriptions of events and things. It is regarded as objectively valid
by most people or as social agreement. The empirical knowledge is a specificknowledge that does not go into general knowledge in society, such as personal
knowledge which are agreed only by less people. For example, suppose a very

delicious hamburger made by a fast food shop located at a place, the informationon this shop’s address is factual knowledge, the knowledge of classification on
fast food shop is general knowledge, and knowledge such as a hamburger madeby this hamburger shop is delicious is empirical knowledge.
As shown in Table 1, most of LGD / OSM is factual knowledge and it iscategorized to factual dataset, but a part of LGD / OSM is categorized into
general knowledge. DBpedia contains both fact data and general knowledge.
However, WordNet contains general and empirical common knowledge.
In this preliminary research, the following issues are suggested.
1. It is necessary to understand data characteristics of coverage and granularityof each dataset, but it is generally hard for large datasets. At this time, wefirstly made a utilization plan on the whole data set, after we examined theavailability of actual data on the premise of these use-case scenarios.
2. Generally, it is tough work to find out correct relations between datasets.
While simple string matching allows us an automatic matching process, theontology mapping cannot be avoid human power at the present. While theaccuracy of this mapping greatly aﬀects the result, mechanical matching processing is diﬃcult. In addition, we built intermediate ontologies and mapped
them to LOD datasets, but building ontology is generally not easy for anovice.
3. Since DBpedia and LGD are datasets made by crowd sourcing, we cannot
expect the completeness and validity of them. Missing or biased data is still
problematic at reasoning. Actually, we found a closed food shop as results.
At this time we attempted to eliminate errors as soon as it was found, but
we need to think about some tools for (semi) automated error checking.
4. The inference procedure was designed according to these use-case scenarios.
For other problems, diﬀerent datasets and diﬀerent work flows may be used.
For example, it depends on features of a target problem about how thebalance should be taken between general knowledge and fact data to solve
the problem.

9Conclusion

In this preliminary research, we made a prototype of action planning system forevents of everyday life and world, based on open knowledge of LOD as fact data
and taxonomy as common knowledge. We utilized a number of large-scale open
databases and knowledge-bases. We found that we had already abundant knowledge about the everyday life and world as diverse open knowledge resources. Thiscondition is very diﬀerent at the era of Good-Old-Fashioned-AI (GOGAI) before
the Web age and LOD. However, we also found that we needed the additional
general and common knowledge that connects such diﬀerent open resources inreasoning action plans with SPARQL endpoints. It is obvious that it will benecessary to make open knowledge more available not only in the verificationand validation for each, but also in the combinations of them for applications.

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